1. Field of the Invention
The present invention relates to a system and method for predicting a cyber threat, and more particularly, to a system and method for collecting various pieces of information, such as Internet security site notice information, network traffic flow information, infringement (hacking) occurrence information, intrusion detection event information, expert-opinion information, etc., generating time-series data and quantitative data, and predicting the frequency, dangerousness, possibility, and time of the occurrence of a cyber threat including hacking, a worm/virus, a Denial of Service (DoS) attack, illegal system access, a malicious code, a social engineering attack, system/data falsification, cyber terror/war, weakness exploitation, etc., to a user using the optimum one of a time-series models and a Delphi method on the data.
2. Discussion of Related Art
Recently, with the rapid development of information and communication technology like the Internet, cyber threats such as computer hacking, viruses, worms, Trojan horses, etc., are increasing. Although there are Intrusion Detection Systems (IDSs), Intrusion Prevention Systems (IPSs), monitoring and control systems, Enterprise Security Management (ESM) systems, etc., to manage and protect against such cyber threats, the systems merely detect a present attack and provide only present network status information. However, since the information is past-use, it is difficult to prevent a threat or enable an administrator or a user to sufficiently cope with a cyber threat.
Therefore, if information on a hacking trend or degree of a cyber threat in the near future was informed in advance to a computer user, akin to a weather forecast, it would help the user to prepare for and cope with a cyber threat. Currently, there exist technologies for network intrusion detection and prevention, network control, ESM, early warning of a cyber threat, etc., but there has not been yet any technology to predict and inform in advance of a cyber threat.